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A training algorithm for classification of high-dimensional data
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DOI：10.1016/S0925-2312(02)00635-5.png)
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Abstract
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We propose an algorithm for training multi layer preceptrons (MLP) for classification problems, that we named hidden layer learning vector quantization. It consists of applying learning vector quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rutherford backscattering spectra and to a benchmark problem of image recognition. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords:
classification
learning vector quantization
hidden layer leaming vector quantization
feature extraction
Rutherford backscattering
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